東京工業大学 · 공학
타카미치 나카모토 교수의 연구실은 가상현실에서의 후각 인식 기술과 고감도 센서 기반의 화학 감지 기술을 핵심으로 연구를 진행하고 있습니다. 특히 퀀텀 크리스탈 마이크로벌런스(QCM)를 활용한 냄새 감지 및 제어 시스템 개발과 함께, 다성분 냄새 혼합 기술을 통해 현실감 있는 후각 환경을 구현하는 데 주력하고 있습니다. 또한 기계학습 기반의 냄새 인상 예측 모델 개발을 통해 인간의 후각 인식을 정량화하고자 하는 응용 연구도 진행 중입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
It's long been possible to give users outside an actual environment that environment's visual and auditory information and thus contribute to establishing presence. However, we've yet to establish much presence when users require olfactory information - such as in environments focused on foods, flowers, perfumes, or, in some cases, more offensive smells. Recently, several VR researchers have become interested in olfaction and olfactory displays that present smells in virtual environments (VEs).
The authors analyzed the behavior of a quartz crystal microbalance (QCM) in the experimental environments of air and liquid using a Mason equivalent circuit. It was found that the mass loading effect of QCM could be regarded as an inductance increase, and the analytical equation of the frequency shift, which is valid over the wide range of the thickness of a loading film, was derived. Utilizing this equation, the frequency shift can be predicted without solving the transcendental equation. Furth
The research on olfactory sense in virtual reality has gradually expanded even though the technology is still premature. We have developed an olfactory display composed of multiple solenoid valves. In the present study, an extended olfactory display, where 32 component odors can be blended in any recipe, is described; the previous version has only 8 odor components. The size was unchanged even though the number of odor components was four times larger than that in the previous display. The compl
Quartz Crystal Microbalance (QCM) is one of the many acoustic transducers. It is the most popular and widely used acoustic transducer for sensor applications. It has found wide applications in chemical and biosensing fields owing to its high sensitivity, robustness, small sized-design, and ease of integration with electronic measurement systems. However, it is necessary to coat QCM with a sensing film. Without coating materials, its selectivity and sensitivity are not obtained. At present, this
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTChemical Sensing in Spatial/Temporal DomainsTakamichi Nakamoto and Hiroshi IshidaView Author Information Graduate School of Science and Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan, and Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Kogahei, Tokyo 184-8588, Japan Cite this: Chem. Rev. 2008, 108, 2, 680–704Publication Date (Web):January 26, 2
Recent studies on machine learning technology have reported successful performances in some visual and auditory recognition tasks, while little has been reported in the field of olfaction. In this paper we report computational methods to predict the odor impression of a chemical from its physicochemical properties. Our predictive model utilizes nonlinear dimensionality reduction on mass spectra data and performs the clustering of descriptors by natural language processing. Sensory evaluation is
An experiment of odor identification using a neural network is described. A quartz-resonator array with different coating films was used, and its output pattern was recognized using a neural network. Various odors of commercially available liquors were identifiable by the network following training. The sensing system was adaptive to environmental variations during the cyclic process of the data sampling and training. High recognition probability was maintained even under temperature variations.
